In traditional mobile networks, trust between subscribers and their serving networks relies on a hardware root of trust: the Subscriber Identity Module (SIM). Conversely, trust between service and home networks is established via Trusted Third Parties (TTPs), known as Clearing Houses (CHs). The 6G environment will witness a substantial increase in subscriber numbers, driven by the mass deployment of the Internet of Everything (IoE) and improvements in network performance. Simultaneously, the performance capabilities required of TTPs to manage trustworthy operator-to-operator (O2O) interactions in 6G must align with the demands of the 6G ecosystem. This work focuses on enhancing CH intermediation capabilities to support O2O trustworthy interactions within the 6G context. Given the close connection between performance and trustworthiness, this paper explores these aspects by modeling interactions between communication parties using a Petri Net model. This model is applied to analyze the quantitative relationships among the non-functional requirements of future 6G communication scenarios, considering both traditional and blockchain-based approaches.

In the last decades, the remarkable impact achieved by Artificial Intelligence (AI) in business and industry has not been mirrored in critical real-world applications. The industrial diffusion of AI in healthcare is facing some resistance due to the lack of uniform legal frameworks and general scepticism among society and medical personnel. This paper proposes a multidisciplinary approach to fill the gap between the theoretical AI-based framework and real clinical practice, tailored to the problem of Posterior Urethral Valves (PUVs) diagnosis in paediatric patients. The multidisciplinary core of the work allows tackling the problem not only under the technical lens, but also from a clinical and industrial perspective: through the adoption of classifier composition mechanisms, this study presents the lessons learned in developing a reliable PUV classifier, as well as in its empirical assessment against real-world data and within a structured diagnostic process. The main contribution of this study is the design of a clinical decision support system for medical experts, which evaluates the behaviour of the model clinically and validates the extracted rules using explainability techniques on real-world data. AI classifiers leveraging vertical training of specialised models were adopted, achieving an overall accuracy of 70 %.

The availability of clean and safe drinking water is essential for public health and sustainable development. This study uses a robust machine learning-based methodology to predict water potability using genuine water quality parameters. The methodology adopted consists of intelligent feature selection along with comparative evaluation of multiple classifiers. As a novelty, this study employs a hybrid resampling technique (Synthetic Minority Over-sampling Technique combined with Edited Nearest Neighbors (SMOTEENN)) that integrates Synthetic Minority Over-sampling Technique (SMOTE) and Edited Nearest Neighbors (ENN) techniques that improve class balance based on noise reduction and oversampling. The experimental results on the public water quality dataset show strong and well-rounded performance across many performance metrics, with most models achieving high scores based on general predictive quality. Further, visualizations such as Receiver Operating Characteristic (ROC) curves and feature importance plots support interpretability and offer insights regarding model behaviour. The main contribution of this paper is in finding cost-effective and scalable solutions for smart water quality monitoring and decision support in public health and environmental safety systems: the proposed approach performs better with the respect to the current state of the art.

Software quality is a critical factor for the overall success and acceptability of software products. To evaluate software quality effectively, standardized models consider both major characteristics and sub-features of the software. As these sub-features often conflict with one another, a crisp and exact approach is neither feasible nor effective. The novelty of this study is to evaluate the quality of Microsoft Word using a hierarchical three-level model based on a Fuzzy Inference System (FIS) aligned with the International Organization for Standardization (ISO)/International Electrotechnical Commission (IEC) 25010 standard. Expert judgments were employed to determine weights for the relative importance of each quality attribute to enhance the realism and accuracy of the assessment. The research establishes that software quality is a hierarchical and dynamic concept, whereby the quality of components across various phases of development directly impacts the quality of the final product and stresses the role played by formal quality evaluation models in guiding the growth and selection of effective and user-oriented software solutions.

One of the key indicators of ecosystem dynamics is the presence of vegetation patterns, which provide valuable insights into the interactions between biotic and abiotic factors. Recognising these patterns is critical for ecosystem resilience studies; automated analysis of satellite imagery is a cornerstone in environmental monitoring, requiring the capability to detect objects on the Earth’s surface. One of the first steps in image object detection is segmentation. This study presents SILVIA (Segmentation and Identification for satelLite Vegetation pattern ImAges), a novel pipeline that leverages the Segment Anything Model (SAM) for automated segmentation of vegetation structures in satellite images. SILVIA generates segmentation masks for spectral images and vegetation index-derived representations; masks can be filtered using confidence thresholds and spatial criteria tailored to the morphology of Fairy Rings. SILVIA, after the multistep filtering and characterisation process, also implements image fusion mechanisms, providing a more robust approach regarding false-positive and false-negative segments: this constitutes the main original contribution of this work. The results demonstrate that this approach effectively identifies fairy rings with minimal human intervention, improving the scalability and reproducibility of vegetation pattern monitoring.

The growing interest in artificial intelligence applications in real clinical practice has made the development of personalised medicine possible. Digital Twins support diagnosis and treatment by providing an overall view of patients’ health status. The definition of a patient’s digital model requires the integration of different sources of information that contribute to a holistic view of the subject. In this work, we propose a preliminary step toward the definition of paediatric digital twins, providing a tool for Region of Interest identification on X-ray images. In detail, the proposed tool, STELLA (Segmentation Tool for Enhanced Localisation and Labelling of diagnostic Areas), is adopted to automatically detect the bladder and urethra regions on the images obtained from the cystourethrography exam. STELLA pipeline is based on Segment Anything Model (SAM) for the segmentation task and Resnet-18 for masks classification: SAM is leveraged for automatic masks generation and ResNet18 is trained on labelled masks for Regions of Interest classification. This is framed in a larger context, whose aim is to support posterior urethral valves diagnosis.

In this era of huge data availability, data-driven approaches are affirming themselves as one of the dominant paradigm in model identification. Due to their capability to fit acquired data, these models exhibit flexibility and the ability to cope with undiscovered knowledge. This paper proposes a method to overcome existing limitations in the model and parameter identification of complex stochastic Time-Series, enabling the identification of processes characterisable according to the Gaussian Mixture Model. More concretely, this paper aims to define methods for learning and classifying the model of complex stochastic processes.

Recently, the field of anesthesiology has increasingly recognized the need for personalized medicine, aiming to tailor drug administration based on the unique physiological and clinical profiles of individual patients. This approach is particularly crucial in the administration of anaesthetic drugs, where inter-individual variability in response can significantly affect both the efficacy and safety of treatment. The main focus of this paper is on the relationship between the healthcare domain and innovative Machine Learning technologies. The specific case study analysed concerns the implementation of a predictive Bayesian Network (BN) model for the inductive administration of Propofol, an anaesthetic drug. The available data is taken from the PhysioNet platform and it relates to a study conducted on nine healthy volunteers who underwent drug administration for approximately three hours, measuring their vital parameters. The results indicate that the choice of the Bayesian Network (BN) formalism is highly suitable for the analysed case study. In conclusion, it is hypothesized that an analysis focused on patient-specific characteristics, such as gender, age, and medical history, could significantly improve the model’s accuracy.

In the medical domain, the early detection and monitoring of specific diseases require both accuracy and interpretability to support clinical decisions. Human digital twin systems are increasingly used in this context, but their adoption often requires data-intensive process for the learning tasks and a high degree of explainability to ensure clinical reliability. To address these challenges, we propose a pipeline that prioritises lightweight and explainability, to extract actionable knowledge from patient data in terms of rules. The approach follows a traditional Machine Learning methodology, including data pre-processing, followed by the construction and validation of a classification model. A rule extraction phase is then introduced to make the classifier’s decision process interpretable. The reliability of the pipeline was evaluated by extracting decision rules for the detection of kidney damage in patients with Congenital Solitary Functioning Kidney. Through the analysis of patient data and the use of a Random Forest classifier, key clinical parameters (e.g., creatinine levels, Holter monitor measurements, and kidney volume) were identified as fundamental to support accurate and reliable diagnoses in clinical practice.

Modern public and private healthcare structures are facing the problem of improving the quality of patient health without increasing costs. Smart healthcare is currently transforming the traditional medical practices, resulting in a more efficient, convenient and personalized healthcare. In this paper, a solution for a fair usage of economic resources is proposed: the FIDCARE approach. Based on a flexible software architecture, with the capability to be extended by external “plugins”, the FIDCARE platform conjugates both the needs. IoT technologies and AI algorithms are at the basis of the entire platform to enable a proper level of flexibility. The paper presents the approach with the case study of oncological therapy.

As healthcare applications of Artificial Intelligence are growing more and more, the necessity of building universal and transparent models in healthcare is of a paramount importance. Defining approaches able to combine the flexibility of data-driven and the explainability of model-based methods is still an open and promising challenge. This paper proposes an approach to combine mathematical modelling, based on Ordinary Differential Equations, Machine Learning and Fluid Stochastic Petri Nets. By adopting the proposed method and tools, a user could understand the effects of drugs and other treatments on a patient in an interpretable manner. The paper also presents a preliminary application of the approach on the human heartbeat.

The goal of this research is to create an intelligent system to assess the manufacturing system’s level of risk for wheat four products. Five Fuzzy Inference Systems (FISs) are arranged in two layers of the model to assess the risk associated with a system that produces wheat four products. There are four FISs with three criteria (Occurrence, Severity, and Detectability) in the model’s first layer. The final input for the manufacturing system will be determined from every physical, chemical, biological, and environmental failure. The suggested model, which is based on Mamdani FISs, ranks the manufacturing systems for wheat four products according to their performance. A four-step approach (i.e., eliciting hazard information for experts, fuzzification, inference, and defuzzification) brings to an evaluation of the final risk level in a real-world wheat four manufacturing system to 22.5%, which shows a fair situation, and it represents a manufacturing system with a high-risk level.

In healthcare, Digital Twins (DTs) promise to personalise treatment plans, simulate surgeries, and forecast individual responses to particular therapies. By adopting Machine Learning methodologies, it is possible to figure out some insights hidden among the features for enhancing medical diagnosis. Our contribution leverages the role of intraoperative ultrasound in liver surgery in building a Bayesian Network (BN) model for enabling the early localisation of hepatic cancer. Under this premise, we aim to determine how a possible diagnosis error could be affected by factors such as age, gender, and before-surgery treatment. The mean to this objective is the construction of a BN model by using both an explicit top-down approach and parameter learning approaches. This is the first step toward the DT definition of a patient affected by hepatic cancer in charge of continuously monitoring the health status.

Human Digital Twin is an emerging technology that could revolutionize the current healthcare system by enabling the delivery of Personalized Health Services through the use of tools such as Artificial intelligence. However, the considerable complexity of the structure of the human body, brought about by continuous molecular and physiological changes, makes it extremely difficult to process medical data extracted by Artificial intelligence techniques. The latter requires a large amount of data for reliable performance, which is often difficult to obtain due to limited quality and availability. In this paper, we propose a methodology to generate Artificial medical data. In detail, we focus on generating Artificial voice signals. The analysis of voice recordings is fundamental to diagnose specific pneumo-articulatory apparatus diseases, such as dysphonia. The generative neural network employed is based on the WaveNet model, due to its autoregressive sampling, which enables generating recordings of variable length. We propose a setup which enables to generate Artificial samples of required sex and pathology to balance and augment the dataset using only one generative network. The quality of the generative network is assessed by balancing the training dataset by generated data and training a convolutional classifier, which is tested on a dataset which was not introduced to the generative network during training. We achieved reasonable improvements in classification accuracy, particularly for the under-represented sex in terms of accuracy, arguing that this approach is worthy of future research.

Recently, Large Language Models (LLMs) have seen some impressive leaps, achieving the ability to accomplish several tasks, from text completion to powerful chatbots. The great variety of available LLMs and the fast pace of technological innovations in this field, is making LLM assessment a hard task to accomplish: understanding not only what such a kind of systems generate but also which is the quality of their results is of a paramount importance. Generally, the quality of a synthetically generated object could refer to the reliability of the content, to the lexical variety or coherence of the text. Regarding the quality of text generation, an aspect that up to now has not been adequately discussed is concerning the readability of textual artefacts. This work focuses on the latter aspect, proposing a set of experiments aiming to better understanding and evaluating the degree of readability of texts automatically generated by an LLM. The analysis is performed through an empirical study based on: considering a subset of five pre-trained LLMs; considering a pool of English text generation tasks, with increasing difficulty, assigned to each of the models; and, computing a set of the most popular readability indexes available from the computational linguistics literature. Readability indexes will be computed for each model to provide a first perspective of the readability of textual contents artificially generated can vary among different models and under different requirements of the users. The results obtained by evaluating and comparing different models provide interesting insights, especially into the responsible use of these tools by both beginners and not overly experienced practitioners.

Abstract MDE enables the centrality of the models in semi-automated development processes. However, its level of usage in industrial settings is still not adequate for the benefits MDE can introduce. This paper proposes a semi-automatic approach for the completion of high-level models in the lifecycle of critical systems, which exhibit an event-driven behaviour. The proposal suggests a specification guideline that starts from a partial SysML model of a system and on a set of requirements, expressed in the well-known Given–When–Then paradigm. On the basis of such requirements, the approach enables the semi-automatic generation of new SysML state machines model elements. Accordingly, the approach focuses on the completion of the state machines by adding proper transitions (with triggers, guards and effects) among pre-existing states. Also, traceability modelling elements are added to the model. Two case studies demonstrate the feasibility of the proposed approach.

Even if the verification of authentication protocols can be achieved through formal analysis, the modelling of such an activity is an error-prone task due to the lack of automated and integrated processes. This paper relies on Unified Modeling Language (UML) profiling and model-transformation techniques to enable automatic analysis of authentication protocols starting from high-level models. The original contribution of this paper is a concrete toolchain, based on a modular approach, to support the modelling and analysis of authentication protocols. In particular, we propose three nested Extended Backus-Naur Form (EBNF) grammars for the high-level specification of the protocol and a transformation from the high-level specification into a specific target language, that is Alice & Bob extended (AnBx). The generated AnBx model can be then formally checked with the Open-Source Fixed-Point Model Checker (OFMC) tool. The validity and necessity of the proposed toolchain is demonstrated with a case study taken from the literature.

Human-Machine Interfaces (HMIs) are getting more and more important in a hyper-connected society. Traditional HMIs are built considering cognitive features while emotional ones are often neglected, bringing sometimes such interfaces to misuse. As a part of a long run research, oriented to the definition of an HMI engineering approach, this paper concretely proposes a method to build an emotional-aware explicit model of the user starting from the behaviour of the human with a virtual agent. The paper also proposes an instance of this model inference process in voice assistants in an automatic depression context, which can constitute the core phase to realize a Human Digital Twin of a patient. The case study generated a model composed of Fluid Stochastic Petri Net sub-models, achieved after the data analysis by a Support Vector Machine.

In a world whose attention to environmental and health problems is very high, the issue of properly managing nuclear waste is of a primary importance. Information and Communication Technologies have the due to support the definition of the next-generation plants for temporary storage of such wasting materials. This paper investigates on the adoption of one of the most cutting-edge techniques in computer science and engineering, i.e. Digital Twins, with the combination of other modern methods and technologies as Internet of Things, model-based and data-driven approaches. The result is the definition of a methodology able to support the construction of risk-aware facilities for storing nuclear waste.

The integration of the Internet into industrial plants has connected Industrial Control Systems (ICS) worldwide, resulting in an increase in the number of attack surfaces and the exposure of software and devices not originally intended for networking. In addition, the heterogeneity and technical obsolescence of ICS architectures, legacy hardware, and outdated software pose significant challenges. Since these systems control essential infrastructure such as power grids, water treatment plants, and transportation networks, security is of the utmost importance. Unfortunately, current methods for evaluating the security of ICS are often ad-hoc and difficult to formalize into a systematic evaluation methodology with predictable results. In this paper, we propose a practical method supported by a concrete toolkit for performing penetration testing in an industrial setting. The primary focus is on the Modbus/TCP protocol as the field control protocol. Our approach relies on a toolkit, named MOSTO, which is licensed under GNU GPL and enables auditors to assess the security of existing industrial control settings without interfering with ICS workflows. Furthermore, we present a model-driven framework that combines formal methods, testing techniques, and simulation to (formally) test security properties in ICS networks.

Abstract The emergence of 5G technologies opens up new opportunities for railway communications. One of the foundational aspects of 5G architecture is its control-plane programmability, which can be achieved through Software Defined Networking (SDN). In railway scenarios, this can be used to dynamically reconfigure the network for a more effective and efficient management of communication flows produced by moving trains. The paper presents a framework for integrating modelling and analysis tools into a programmable control plane specifically tailored to railway communications. We introduce the concept of domain-awareness in the network control plane as an SDN-enabled feature that allows achieving application-specific advantages besides those purely expressed in terms of key performance indicators such as the quality of service. We propose a reference architecture in which domain-awareness in the control plane is obtained by considering information gathered by network devices and ad-hoc communication gateways that are able to detect relevant signalling events. In the architecture, the actual behaviour of the SDN controller is governed by applications that are able to react to specific triggers and re-configure network devices accordingly. We also provide a methodological framework based on model-driven engineering and formal methods, including dynamic state machines, for the automatic generation of SDN control plane logic.

Currently, research in Artificial Intelligence, both in Machine Learning and Deep Learning, paves the way for promising innovations in several areas. In healthcare, especially, where large amounts of quantitative and qualitative data are transferred to support studies and early diagnosis and monitoring of any diseases, potential security and privacy issues cannot be underestimated. Federated learning is an approach where privacy issues related to sensitive data management can be significantly reduced, due to the possibility to train algorithms without exchanging data. The main idea behind this approach is that learning models can be trained in a distributed way, where multiple devices or servers with decentralized data samples can provide their contributions without having to exchange their local data. Recent studies provided evidence that prototypes trained by adopting Federated Learning strategies are able to achieve reliable performance, thus by generating robust models without sharing data and, consequently, limiting the impact on security and privacy. This work propose a literature overview of Federated Learning approaches and systems, focusing on its application for healthcare. The main challenges, implications, issues and potentials of this approach in the healthcare are outlined.

Distributed Ledger Technology is demonstrating its capability to provide flexible frameworks for information assurance capable of resisting to byzantine failures and multiple target attacks. The availability of development frameworks allows the definition of many applications using such a technology. On the contrary, the verification of such applications are far from being easy since testing is not enough to guarantee the absence of security problems. The paper describes an experience in the modelling and security analysis of one of these applications by means of formal methods: in particular, we consider the Tweetchain protocol as a case study and we use the Tamarin Prover tool, which supports the modelling of a protocol as a multiset rewriting system and its analysis with respect to temporal first-order properties. With the aim of making the modeling and verification process reproducible and independent of the specific protocol, we present a general structure of the Tamarin Prover model and of the properties to verified. Finally, we discuss the strengths and limitations of the Tamarin Prover approach considering three aspects: modelling, analysis and the verification process. Copyright

The growing number of people suffering from depression makes it increasingly necessary to find new approaches able to support medical experts in its diagnosis. The early detection of depressive symptoms is crucial in limiting the co-occurrence of associated behavioural disorders such as psycho-motor retardation symptoms and social withdrawal. Therefore, automatic detection systems represent promising solutions not only for supporting the early diagnosis of the disease but also for monitoring patient’s health status, thus improving both the quality of the care process and life quality of patients. At the light of these considerations, this paper proposes an automatic system exploiting a machine learning algorithm, to distinguish among depressed and healthy subjects through the analysis of selected acoustic features extracted from spontaneous speech narratives produced by healthy and depressed subjects. The proposed system achieves a classification accuracy of about 85%, proving to be a promising solution for supporting the diagnosis of depression in real-time in a reliable, fast, inexpensive and non-intrusive ways.

Federated Learning is a valuable instrument for building AI-based systems that preserve the privacy and security of sensitive data, based on the main concept of shifting no more the data to the edges but moving computations to data, avoiding the collection, sharing, and use of such data by third parties. More robust federated learning systems should be able of preventing malicious inference over both data exchanged during training and the final trained model while ensuring the resulting model also has acceptable predictive accuracy. This study proposes a preliminary analysis to investigate and evaluate the effectiveness and efficiency of a federated approach to ensure valid classification accuracy and data security. A real case study from the ANDROIDS project, concerning the application of machine learning-based systems for supporting mental-health disorders detection, was considered. Large amounts of sensitive patient information are collected, which must be obfuscated or anonymized to provide a preliminary level of protection. Unfortunately, the real bottleneck lies in the difficulty of extracting all sensitive data for anonymization, due to a lot of data to handle as well as the considerable effort required. We propose a Natural Language Processing approach for sensitive knowledge detection and classification, performed by adopting a federated approach. Accuracy decay and latency introduced by applying a decentralized learning approach compared to the same task and data performed in a centralized way were evaluated. Preliminary results proved that effectiveness can be reached by a correct tuning of the federated algorithm and by choosing the right number of participants to the federation.

Recent years have seen the widespread adoption of Artificial Intelligence techniques in several domains, including healthcare, justice, assisted driving and Natural Language Processing (NLP) based applications (e.g., the Fake News detection). Those mentioned are just a few examples of some domains that are particularly critical and sensitive to the reliability of the adopted machine learning systems. Therefore, several Artificial Intelligence approaches were adopted as support to realize easy and reliable solutions aimed at improving the early diagnosis, personalized treatment, remote patient monitoring and better decision-making with a consequent reduction of healthcare costs. Recent studies have shown that these techniques are venerable to attacks by adversaries at phases of artificial intelligence. Poisoned data set are the most common attack to the reliability of Artificial Intelligence approaches. Noise, for example, can have a significant impact on the overall performance of a machine learning model. This study discusses the strength of impact of noise on classification algorithms. In detail, the reliability of several machine learning techniques to distinguish correctly pathological and healthy voices by analysing poisoning data was evaluated. Voice samples selected by available database, widely used in research sector, the Saarbruecken Voice Database, were processed and analysed to evaluate the resilience and classification accuracy of these techniques. All analyses are evaluated in terms of accuracy, specificity, sensitivity, F1-score and ROC area.

Abstract The current travel demand in railways requires the adoption of novel approaches and technologies in order to increase network capacity. Virtual Coupling is considered one of the most innovative solutions to increase railway capacity by drastically reducing train headway. The aim of this paper is to provide an approach to investigate the potential of Virtual Coupling in railways by composing stochastic activity networks model templates. The paper starts describing the Virtual Coupling paradigm with a focus on standard European railway traffic controllers. Based on stochastic activity network model templates, we provide an approach to perform quantitative evaluation of capacity increase in reference Virtual Coupling scenarios. The approach can be used to estimate system capacity over a modelled track portion, accounting for the scheduled service as well as possible failures. Due to its modularity, the approach can be extended towards the inclusion of safety model components. The contribution of this paper is a preliminary result of the PERFORMINGRAIL (PERformance-based Formal modelling and Optimal tRaffic Management for movING-block RAILway signalling) project funded by the European Shift2Rail Joint Undertaking.

Artificial Intelligence systems have enabled significant benefits for users and society, but whilst the data for their feeding are always increasing, a side to privacy and security leaks is offered. The severe vulnerabilities to the right to privacy obliged governments to enact specific regulations to ensure privacy preservation in any kind of transaction involving sensitive information. In the case of digital and/or physical documents comprising sensitive information, the right to privacy can be preserved by data obfuscation procedures. The capability of recognizing sensitive information for obfuscation is typically entrusted to the experience of human experts, who are over-whelmed by the ever increasing amount of documents to process. Artificial intelligence could proficiently mitigate the effort of the human officers and speed up processes. Anyway, until enough knowledge won't be available in a machine readable format, automatic and effectively working systems can't be developed. In this work we propose a methodology for transferring and leveraging general knowledge across specific-domain tasks. We built, from scratch, specific-domain knowledge data sets, for training artificial intelligence models supporting human experts in privacy preserving tasks. We exploited a mixture of natural language processing techniques applied to unlabeled domain-specific documents corpora for automatically obtain labeled documents, where sensitive information are recognized and tagged. We performed preliminary tests just over 10.000 documents from the healthcare and justice domains. Human experts supported us during the validation. Results we obtained, estimated in terms of precision, recall and F1-score metrics across these two domains, were promising and encouraged us to further investigations.

The article revises the different views and conceptualizations for cyber resilience and proposes a novel holistic definition able to accommodate in a unique coherent vision the existing multiple facets. The resulting integrated concept has been formalisied defining a new comprehensive domain-based ontology for Cyber Resilience to be used as a knowledge base for an effective data driven multi-criteria decision making for resilience.

Model-driven techniques can be used to automatically produce formal models from different views of a system realised by using several modelling languages and notations. Specifications are transformed into formal models so facilitating the analysis of complex system for design, validation or verification purposes. However, no single formalism suits for representing all system’s views. In particular, the assessment of non-functional properties often requires integrated modelling approaches. The ultimate goal of the research work described in this paper is to develop a comprehensive, theoretical and practical framework able to support the development and the integration of new or existing model-driven approaches for the automatic generation of multi-formalism models. This paper defines the core theoretical ideas on which the framework is based and demonstrates their concrete applicability to the development of a multi-formalism approach for performability assessment.

Due to its simplicity and ease of application, testing is the main technique by which complex safety-critical systems can be verified in order to find both omission and commission bugs. Strict requirements on such systems, joined to the necessity to re-execute the test set in the regression testing campaign, provokes a test case set and testing time explosion that can be tackled only by means of the use of parallel independent testing environments. Parallelism in such environments is not easy to accomplish due to the heterogeneity of processes, methodologies and tools. Service Oriented Architecture (SOA) is a key factor in the development of an organic modelling and execution methodology in order to build a heterogeneous and distributed environment that supports a system testing. In this paper we propose an adoption of a classical SOA reference architecture in order to address the build of such an environment for safety-critical control systems. Moreover we provide indications on the integration of SOA specific architecture components with existing centralized testing environments providing an example in signalling railway control systems.